‪Can a multi-camera system be used for localization, mapping, and obstacle detection in self-driving cars to within 10 cm of accuracy? Whither lidar?‬
It seems that LIDAR presents a problem for resolving the car's environment at higher speed. While I'm not too familiar with the dynamics of LIDAR I do know that it's a physical system that relies on sending and receiving laser pulses to various points around the car by way of rotating mirrors. As speeds increase, it seems different arrangements of mirrors and light collectors might have to be used to maintain a high-resolution image. There's some evidence that Doppler LIDAR (developed in the 1990s) became less accurate with higher velocities. However, LIDAR is partly preferred over radar because of its higher accuracy even when tracking objects at high speeds - this is why LIDAR guns are increasingly being used by police instead of radar guns to track speeding vehicles. It seems natural that a set of high-resolution cameras paired with a well-trained neural network would not be subject to the same physical limitations as LIDAR.

I think that an important intuition to consider is that while LIDAR is used to generate clouds of data points whose shapes and patterns can be analyzed by autonomous car software, cameras can pick up non-topograhical features such as road lines, the content of roadsigns, and additional location context such as storefronts and intersection layouts. Considering that these cameras can use pattern recognition and stereoscopy to also generate a 3D topographic map of the environment, it seems plausible that Level 5 self driving cars would not require LIDAR.

Here's an interesting look at the problem.